Intelligent systems have been developed which are intended to behave autonomously, automate tasks in an intelligent manner, and extend human knowledge. These systems are designed and modeled based on essentially three distinct fields of technology known, respectively, as
(1) artificial intelligence (AI);
(2) artificial neural networks (ANNs); and
(3) brain-based devices (BBDs).
The intelligent systems based on AI and ANN include digital computers which are programmed to perform tasks as far ranging as playing chess to robotics. AI algorithms are logic-based and preprogrammed to carry out complex algorithms implemented with detailed software instructions. ANNs are an oversimplified abstraction of biological neurons that do not take into consideration nervous system structure (i.e. neuroanatomy) and often require a supervisory or teacher signal to get desired results. BBDs, on the other hand, are based on different principles and a different approach to the development of intelligent systems.
BBDs are based on fundamental neurobiological principles and are modeled after the brain bases of perception and learning found in living beings. BBDs incorporate a simulated brain or nervous system with detailed neuroanatomy and neural dynamics that control behavior and shape memory. BBDs also have a physical instantiation, called a morphology or phenotype, which allows active sensing and autonomous movement in the environment. BBDs, similar to living beings, organize unlabeled signals they receive from the environment into categories. When a significant environmental event occurs, BBDs, which have a simulated neuronal area called a value system, adapt the device's behavior.
The different principles upon which logic-based intelligent systems and BBDs operate are significant. As powerful as they are, logic-based machines do not effectively cope with novel situations or process large data sets simultaneously. By their nature, novel situations cannot be programmed beforehand because these typically consist of unexpected and varying numbers of components and contingencies. Furthermore, situations with broad parameters and changing contexts can lead to substantial difficulties in programming. And, many algorithms have poor scaling properties, meaning the time required to run them increases exponentially as the number of input variables grows.